Add paper link, GitHub repository, and dataset description

Hi! I'm Niels from the Hugging Face community science team.

This PR improves the dataset card by adding:
- Metadata for task categories and license.
- A link to the associated paper: [Demystifying Reinforcement Learning for Long-Horizon Tool-Using Agents: A Comprehensive Recipe](https://huggingface.co/papers/2603.21972).
- A link to the official [GitHub repository](https://github.com/WxxShirley/Agent-STAR).
- A structured description of the synthetic queries and the specific files included in this repository.

This helps users discover and cite your work correctly on the Hub.

README.md

@@ -1,3 +1,52 @@
----
-license: mit
----
+---
+license: mit
+task_categories:
+- other
+tags:
+- reinforcement-learning
+- tool-use
+- llm-agents
+---
+
+# Agent-STAR TravelDataset
+
+This repository contains the synthetic queries and datasets presented in the paper [Demystifying Reinforcement Learning for Long-Horizon Tool-Using Agents: A Comprehensive Recipe](https://huggingface.co/papers/2603.21972). 
+
+The dataset serves as a long-horizon tool-use testbed for Reinforcement Learning (RL) research, specifically utilizing the TravelPlanner environment.
+
+- **Paper:** [Demystifying Reinforcement Learning for Long-Horizon Tool-Using Agents: A Comprehensive Recipe](https://huggingface.co/papers/2603.21972)
+- **GitHub Repository:** [WxxShirley/Agent-STAR](https://github.com/WxxShirley/Agent-STAR)
+
+## Dataset Description
+
+The dataset includes over **17K** synthetic queries generated through a three-step pipeline: element sampling, feasibility checking, and LLM back-translation. These queries require agents to iteratively call tools to satisfy multifaceted constraints.
+
+### File Information
+
+| Data | Description |
+|---|---|
+| `TravelPlanner_Val180.jsonl` | Official TravelPlanner validation set of 180 instances |
+| `TravelTotal_17K.jsonl` | All 17K+ synthetic queries after element sampling, feasibility checking, and back-translation |
+| `Travel_Mixed_1K_RL.jsonl` | Default 1K RL training set with mixed difficulty |
+| `Travel_Easy_1K.jsonl` | Difficulty-specific 1K set (Easy) for controlled experiments |
+| `Travel_Medium_1K.jsonl` | Difficulty-specific 1K set (Medium) for controlled experiments |
+| `Travel_Hard_1K.jsonl` | Difficulty-specific 1K set (Hard) for controlled experiments |
+
+## Related Resources
+
+- **Travel Database:** [Agent-STAR-TravelDatabase](https://huggingface.co/datasets/xxwu/Agent-STAR-TravelDatabase) (Required for environment interactions)
+- **Models:** [Agent-STAR Collection](https://huggingface.co/collections/xxwu/agent-star)
+
+## Citation
+
+```bibtex
+@misc{wu2026agentstar,
+      title={Demystifying Reinforcement Learning for Long-Horizon Tool-Using Agents: A Comprehensive Recipe}, 
+      author={Xixi Wu and Qianguo Sun and Ruiyang Zhang and Chao Song and Junlong Wu and Yiyan Qi and Hong Cheng},
+      year={2026},
+      eprint={2603.21972},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2603.21972}, 
+}
+```